Panorama

Given different images of the same scene, it generates a panorama.

Overview

This project implements an end-to-end panorama generation system that combines multiple photographs taken from a fixed viewpoint into a single, seamlessly blended panoramic image. The implementation handles feature detection, image alignment, and blending to create high-quality panoramas.

Features

• Automatic Feature Detection - Identifies corresponding points across multiple images
• Image Registration - Aligns images using homography transformations
• Seamless Blending - Combines images with smooth transitions to avoid visible seams
• Visualization - Step-by-step visualization of the panorama generation process

Project Structure

• A3.ipynb - Main Jupyter notebook containing the panorama generation implementation (Part 1 A)
  • Includes complete pipeline from image loading through final panorama output
  • Includes visualizations of intermediate processing steps
  • Demonstrates the core algorithms used for stitching and blending

Requirements

• Python 3.x
• Jupyter Notebook
• OpenCV (cv2) - for image processing
• NumPy - for numerical operations
• Matplotlib - for visualization

Usage

Using Example Images

1. Open A3.ipynb in Jupyter Notebook or JupyterLab
2. The notebook contains example image files that demonstrate the panorama generation pipeline
3. Run the notebook cells to see how the system processes images and generates a panorama
4. View the visualization outputs to see intermediate results and the final panorama

Using Your Own Photos

To generate a panorama with your own images:

1. Prepare Your Images

   • Take multiple photos of the same scene from a fixed point
   • Ensure there is significant overlap between adjacent images (30-50% overlap recommended)
   • Use consistent exposure and focus settings across all images
   • Save your images in a common format (JPEG, PNG, etc.)

2. Upload to the Project

   • Create a folder in the repository (e.g., my_images/)
   • Upload your image files to this folder
   • Note the file paths or filenames

3. Modify the Notebook

   • Open A3.ipynb in Jupyter Notebook
   • Locate the cell that loads the example images
   • Replace the image file paths with paths to your own images
   • Update any image list or array variables to include your images in the correct order (left to right)

4. Run the Pipeline

   • Execute the notebook cells with your image paths
   • The system will detect features, match them across images, and generate your custom panorama
   • Review the visualization outputs to verify the results

How It Works

The panorama generation process typically involves:

1. Feature Detection - Detect keypoints in each image using algorithms like SIFT or ORB
2. Feature Matching - Match corresponding features across image pairs
3. Homography Estimation - Calculate perspective transforms to align images
4. Image Blending - Merge images smoothly to create the final panorama
5. Visualization - Display results at each stage

Example Images

The repository includes sample image files that are used for demonstration purposes. These example images showcase how the panorama generation algorithm works. You can replace these with your own images following the instructions in the "Using Your Own Photos" section above.

Tips for Best Results

• Use a tripod or stable camera position to minimize unwanted camera movement
• Ensure adequate lighting conditions in your scene
• Avoid taking photos with extreme perspective distortion
• Include enough unique features in the overlapping regions for reliable matching
• Keep the number of images reasonable (typically 2-10 images work best)

License

MIT License